Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations Article Swipe
Quang Minh Nguyen
,
Nguyễn Thị Thu Hương
,
Pham Quoc Khanh
,
La Phu Hien
,
Dieu Tien Bui
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16050819
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16050819
The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI.
Related Topics
Concepts
Bilinear interpolation
Kriging
Downscaling
Digital elevation model
Artificial neural network
Interpolation (computer graphics)
Resampling
Bicubic interpolation
Computer science
Overfitting
Remote sensing
Environmental science
Artificial intelligence
Multivariate interpolation
Geology
Machine learning
Geography
Meteorology
Precipitation
Computer vision
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs16050819
- https://www.mdpi.com/2072-4292/16/5/819/pdf?version=1709029621
- OA Status
- gold
- Cited By
- 25
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392240080
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392240080Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs16050819Digital Object Identifier
- Title
-
Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging InterpolationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-27Full publication date if available
- Authors
-
Quang Minh Nguyen, Nguyễn Thị Thu Hương, Pham Quoc Khanh, La Phu Hien, Dieu Tien BuiList of authors in order
- Landing page
-
https://doi.org/10.3390/rs16050819Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/16/5/819/pdf?version=1709029621Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/16/5/819/pdf?version=1709029621Direct OA link when available
- Concepts
-
Bilinear interpolation, Kriging, Downscaling, Digital elevation model, Artificial neural network, Interpolation (computer graphics), Resampling, Bicubic interpolation, Computer science, Overfitting, Remote sensing, Environmental science, Artificial intelligence, Multivariate interpolation, Geology, Machine learning, Geography, Meteorology, Precipitation, Computer vision, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
- Citations by year (recent)
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2025: 19, 2024: 6Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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